A Kriging-assisted multi-stage evolutionary algorithm for expensive many-objective optimization problems-Reference-Cited by-同舟云学术

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A Kriging-assisted multi-stage evolutionary algorithm for expensive many-objective optimization problems

Published:2024-03-13 Issue:4 Volume:67 Page:
ISSN:1615-147X
Container-title:Structural and Multidisciplinary Optimization
language:en
Short-container-title:Struct Multidisc Optim

Author:

Gu Qinghua,Wang Xueqing,Wang Dan,Liu Di

Funder

National Natural Science Foundation of China

Shaanxi province fund for Distinguished Young Scholars

Metal Mine Intelligent Mining Theory and Technology Innovation Team

Data-driven optimization of multi-process collaborative mining in open-pit mines with through-blast shovel breaking

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00158-024-03748-4.pdf

Reference43 articles.

1. Cai X, Gao L, Li X (2020) Efficient generalized surrogate-assisted evolutionary algorithm for high-dimensional expensive problems. IEEE Trans Evol Comput 24:365–379. https://doi.org/10.1109/TEVC.2019.2919762

2. Cheng R, Jin Y, Olhofer M, Sendhoff B (2016) A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans Evol Comput 20:773–791. https://doi.org/10.1109/TEVC.2016.2519378

3. Cheng R, Li M, Tian Y, Zhang X, Yang S, Jin Y, Yao X (2017) A benchmark test suite for evolutionary many-objective optimization. Complex Intell Syst 3:67–81. https://doi.org/10.1007/s40747-017-0039-7

4. Chugh T, Jin Y, Miettinen K et al (2018) A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization. IEEE Trans Evol Comput 22:129–142. https://doi.org/10.1109/TEVC.2016.2622301

5. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https://doi.org/10.1007/BF00994018

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